The Internet and social media platforms (e.g., Facebook®, Twitter®, blogs) provide authors with an easy-to-use interface for conveying information and opinions. Authors can log-on to these platforms from their personal computers, cell phones, or other communication devices and convey information available to the world within seconds.
Many authors convey information across multiple platforms. For example, an individual may have a Twitter® account, a Facebook® account, and a blog for conveying information. Thus, a author may post an opinion on Facebook® using his/her Facebook® account and then post a similar or related opinion on his/her blog.
Sentiment analysis technology takes advantage of these media and platforms and uses sophisticated tools for analyzing the author data for particular “sentiment” (the term sentiment can refer to an attitude, opinion, and/or emotion towards a particular topic). For example, an author may post on a blog their fondness of the new Apple iPhone®. They could likewise log into their Twitter® account and post a similar opinion. Sentiment analysis extracts this data from the various social media platforms and analyzes it to determine information about the author and associate the author and his/her opinion with a particular sentiment. However, when the author posts opinions on a topic using multiple, different social media platforms, it is difficult to adequately link the author across platforms and determine the author's overall social impact in the world. This is especially true when the author's identity is not as apparent on a particular platform. For example, an author may use his/her real name when posting entries on Facebook® but may use a pseudonym when posting entries on his/her blog. Thus, it would be advantageous to profile the authors on the different social media platforms and automatically link the authors across the multiple, different platforms to determine their overall social impact.